SeSAMe provides extensive native support for the Illumina mouse array (referred to as the MM285 array). The MM285 contains ~285,000 probes covering over 20 design categories including gene promoters, enhancers, CpGs in synteny to human EPIC array as well as other biology. This documents describe the procedure to process the MM285 array.
Let’s download an example mouse array IDAT
res_grn = sesameDataDownload("204637490002_R05C01_Grn.idat")
res_red = sesameDataDownload("204637490002_R05C01_Red.idat")
pfx = sprintf("%s/204637490002_R05C01", res_red$dest_dir)To load IDAT into SigSet, one needs the readIDATpair function,
The default openSesame pipeline works for the mouse array
Let’s load a pre-built SigSet object
Preprocess the sigset to produce beta values. The standard noob, dyeBiasCorrTypeINorm works as expected:
Retrieve beta values using the following commands
By default the repeat and suboptimally designed probes are masked by NA. Starting from mouse array, the suboptimally designed probes take a new probe ID prefix (“uk”) instead of the “cg”/“ch”/“rs” typically seen in the human array.
## [1] 23039
## uk-1-101008622_BC11 uk-11-118757022_TC11 uk-11-4183579_TC11
## NA NA NA
## uk-11-72715146_BC11 uk-12-45422860_BC11 uk-12-49764082_BC11
## NA NA 0.02095679
To use these probes, one skip qualityMask:
## [1] 23039
## uk-1-101008622_BC11 uk-11-118757022_TC11 uk-11-4183579_TC11
## NA NA NA
## uk-11-72715146_BC11 uk-12-45422860_BC11 uk-12-49764082_BC11
## NA NA 0.02095679
Note that probes can still be masked because of insignificant detection p-value One can completely turn off masking by skipping that
## [1] 17514
or use mask=FALSE in the getBetas function.
betas = sset_normalized %>%
qualityMask %>%
detectionMask %>%
getBetas(mask = FALSE)
sum(is.na(betas))## [1] 1
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Let’s load a pre-built SigSet object from SeSAMeData
Calculate beta values using the following commands.
Convert the beta values to Variant Allele Frequencies.
It should be noted that since variant allele frequency is not always measured in green for Infinium-II and M-allele for Infinium-I, one needs to flip the beta values for some probes to calculate variant allele frequency.
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Infer strain information for mouse array. This will return a list containing the best guess, p-value of the best guess, and probabilities of all strains.
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## NOD_ShiLtJ
## 4.868143e-09
Let’s load beta values from SeSAMeData
Compare mouse array data with mouse tissue references. This will return a grid object that contrasts the traget sample with pre-build mouse tissue reference.
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Let’s load beta values from SeSAMeData
The age of the mouse can be predicted using the predicMouseAgeInMonth function. This looks for overlapping probes and estimates age using an aging model built from 321 MM285 probes. The function returns a numeric output of age in months.
The model is most accurate with SeSAMe preprocessing. Here’s an example.
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## [1] 1.413134